TF-UNet: Resolving Complex Speckles for Single-Shot Reconstruction of 512^2-Matrix Images Using a Micron-Sized Optical Fiber

TL;DR

This paper introduces TF-UNet, a deep learning architecture inspired by physics, for high-fidelity single-shot imaging through micron-sized tapered optical fibers, enabling detailed 512x512 image reconstruction.

Contribution

The paper presents a novel TF-UNet architecture with hierarchical grouped-MLP fusion to address intermodal coupling in tapered fibers for improved imaging reconstruction.

Findings

TF-UNet outperforms standard U-Net in fidelity and perceptual quality.

Achieves 512x512 single-shot image reconstruction through micron-sized fibers.

Validated on biological datasets for interpretable imaging.

Abstract

Tapered optical fibers (TFs), with diameters gradually reduced from hundreds of microns to the micron scale, offer key advantages over conventional flat optical fibers (FFs), including uniform illumination, efficient long-range signal collection, and minimal invasiveness for applications in high-sensitivity biosensing, optogenetics, and photodynamic therapy. However, high-fidelity, single-shot imaging through a single TF remains underexplored due to intermodal coupling from the tapering geometry, which distorts output speckle patterns and poses challenges for image reconstruction using existing deep learning methods. Here, we propose a physics-inspired TF-UNet architecture that augments skip connections with hierarchical grouped-MLP fusion to effectively capture non-local, cross-scale dependencies caused by intermodal coupling in TFs. We experimentally validate our method on both FFs and TFs, demonstrating that TF-UNet outperforms standard U-Net variants in structural and perceptual fidelity while maintaining competitive PSNR at quadratic complexity. Our study offers a promising approach for deep learning-based imaging through micron-sized, ultrafine optical fibers, enabling scanning-free single-shot reconstruction on a 512x512 reconstruction matrix, and further validating the framework on biologically meaningful neuronal and vascular datasets for physically interpretable characterization.